def render(nx, ny, my_data):
a = [""]
b = [""]
for i in my_data.keys():
if i.isupper():
a.append(my_data[i])
elif i != 'bank_player_tow' and i != 'bank_player_one':
b.append(my_data[i])
a.append(my_data['bank_player_tow'])
a = a[::-1]
b.append(my_data['bank_player_one'])
data = [a, b]
tb = pl.table(cellText=data, loc=(0, 0), cellLoc='center')
tc = tb.properties()['child_artists']
for cell in tc:
cell.set_height(1 / ny)
cell.set_width(1 / nx)
ax = pl.gca()
ax.set_xticks([])
ax.set_yticks([])
pl.show()
def fit_plot(self, data, topn=0, bins=20):
""" Create a plot. """
from matplotlib import pylab as pl
distros = self.get_topn(topn)
xx = numpy.linspace(data.min(), data.max(), 300)
table = []
nparms = max(len(x.parms) for x in distros)
tcolours = []
for dd in distros:
patch = pl.plot(xx, [dd.pdf(p) for p in xx], label='%10.2f%% %s' % (100.0*dd.rss/dd.dss, dd.name))
row = ['', dd.name, '%10.2f%%' % (100.0*dd.rss/dd.dss,)] + ['%0.2f' % x for x in dd.parms]
while len(row) < 3 + nparms:
row.append('')
table.append(row)
tcolours.append([patch[0].get_markerfacecolor()] + ['w'] * (2+nparms))
# add a historgram with the data
pl.hist(data, bins=bins, normed=True)
tab = pl.table(cellText=table, cellColours=tcolours,
colLabels=['', 'Distribution', 'Res. SS/Data SS'] + ['P%d' % (x + 1,) for x in range(nparms)],
bbox=(0.0, 1.0, 1.0, 0.3))
#loc='top'))
#pl.legend(loc=0)
tab.auto_set_font_size(False)
tab.set_fontsize(10.)
def compareAnimals(animals, precision):
'''
:param animals:动物列表
:param (int) precision: 精度
:return : 表格，包含任意两个动物之间的欧式距离
'''
columnLabels = []
for a in animals:
columnLabels.append(a.getName())
rowLabels = columnLabels[:]
tableVals = []
#循环计算任意两个动物间的欧氏距离
for a1 in animals:
row = []
for a2 in animals:
if a1 == a2:
row.append('--')
else:
distance = a1.distance(a2)
row.append(str(round(distance, precision)))
tableVals.append(row)
#生成表格
table = plt.table(rowLabels=rowLabels,
colLabels=columnLabels,
cellText=tableVals,
cellLoc='center',
loc='center',
colWidths=[0.2] * len(animals))
table.scale(1, 2.5)
plt.axis('off')
plt.savefig('chapter19_1.png', dpi=100)
plt.show()
def fit_plot(self, data, topn=0, bins=20):
""" Create a plot. """
from matplotlib import pylab as pl
distros = self.get_topn(topn)
xx = numpy.linspace(data.min(), data.max(), 300)
table = []
nparms = max(len(x.parms) for x in distros)
tcolours = []
for dd in distros:
patch = pl.plot(xx, [dd.pdf(p) for p in xx],
label='%10.2f%% %s' %
(100.0 * dd.rss / dd.dss, dd.name))
row = ['', dd.name,
'%10.2f%%' % (100.0 * dd.rss / dd.dss, )
] + ['%0.2f' % x for x in dd.parms]
while len(row) < 3 + nparms:
row.append('')
table.append(row)
tcolours.append([patch[0].get_markerfacecolor()] + ['w'] *
(2 + nparms))
# add a historgram with the data
pl.hist(data, bins=bins, normed=True)
tab = pl.table(cellText=table,
cellColours=tcolours,
colLabels=['', 'Distribution', 'Res. SS/Data SS'] +
['P%d' % (x + 1, ) for x in range(nparms)],
bbox=(0.0, 1.0, 1.0, 0.3))
#loc='top'))
#pl.legend(loc=0)
tab.auto_set_font_size(False)
tab.set_fontsize(10.)
def compareAnimals(animals, precision):
'''
:param animals:动物列表
:param (int) precision: 精度
:return : 表格，包含任意两个动物之间的欧式距离
'''
columnLabels = []
for a in animals:
columnLabels.append(a.getName())
rowLabels = columnLabels[:]
tableVals = []
#循环计算任意两个动物间的欧氏距离
for a1 in animals:
row =[]
for a2 in animals:
if a1 == a2:
row.append('--')
else:
distance = a1.distance(a2)
row.append(str(round(distance, precision)))
tableVals.append(row)
#生成表格
table = plt.table(rowLabels=rowLabels,
colLabels=columnLabels,
cellText=tableVals,
cellLoc='center',
loc='center',
colWidths=[0.2]*len(animals))
table.scale(1, 2.5)
plt.axis('off')
plt.savefig('chapter19_1.png', dpi=100)
plt.show()
def export_plot_as_jpeg(self):
print('export_plot_as_jpeg()')
import matplotlib.pylab as plt
ES = self.export_settings
P = self.plot_n_fit
L = self.x_slicer.settings['stop'] - self.x_slicer.settings['start']
plt.figure()
ax = plt.subplot(111)
y_lim = [None, None]
x_lim = [None, None]
for label, (x, y) in self.gather_plot_data_for_export().items():
ax.semilogy(x, y, label=label)
if len(y) == L:
y_lim = [0.9 * y[-1], 1.05 * y[0]]
x_lim = [0.99 * x[0], x[-1] * 1.1]
# Apply limits
if ES['auto_y_lim']:
ax.set_ylim(y_lim)
else:
ax.set_ylim(ES['y_lim_min'], ES['y_lim_max'])
if ES['auto_x_lim']:
ax.set_xlim(x_lim)
else:
ax.set_xlim(ES['x_lim_min'], ES['x_lim_max'])
plt.legend(loc=1)
# Put the fit results somewhere
if ES['include_fit_results']:
tab = plt.table(
cellText=P.get_result_table(),
colWidths=[0.15, 0.1, 0.04],
loc='lower left',
colLoc=['right', 'right', 'left'],
)
tab.auto_set_font_size(True)
for cell in tab.get_celld().values():
cell.set_linewidth(0)
if ES['plot_title'] != '':
plt.title(ES['plot_title'])
plt.xlabel('time ({})'.format(self.settings['time_unit']))
plt.ylabel('intensity (a.u.)')
plt.tight_layout()
fname = self.databrowser.settings['data_filename']
fig_name = fname.replace('.h5', '_{:0.0f}.jpg'.format(time.time()))
plt.savefig(fig_name, dpi=300)
plt.close()
self.databrowser.ui.statusbar.showMessage('exported new data to ' +
fig_name)
def plot(table,x):
ny = len(table)
nx = len(table[0])
pl.figure("Section "+ str(x+1))
tb = pl.table(cellText=table, loc=(0,0), cellLoc='center')
ax = pl.gca()
ax.set_xticks([])
ax.set_yticks([])
pl.show()
def fDisplay(matrix):
pl.figure()
tb = pl.table(cellText=matrix, loc=(0, 0), cellLoc='center')
tc = tb.properties()['child_artists']
for cell in tc:
cell.set_height(1 / ny)
cell.set_width(1 / nx)
ax = pl.gca()
ax.set_xticks([])
ax.set_yticks([])
plt.show()
return ()
def plot(table, secname):
ny = len(table)
nx = len(table[0])
pl.figure("Section " + secname)
tb = pl.table(cellText=table, loc=(0, 0), cellLoc='center')
tc = tb.properties()['child_artists']
for cell in tc:
cell.set_height(1 / ny)
cell.set_width(1 / nx)
ax = pl.gca()
ax.set_xticks([])
ax.set_yticks([])
pl.show()
def main():
plt.figure()
ax = plt.gca()
y = np.random.randn(9)
col_label = ['col1', 'col2', 'col3']
row_label = ['row1', 'row2', 'row3']
table_value = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
row_color = ['red', 'gold', 'green']
my_table = plt.table(cellText=table_value,
colWidths=[0.1] * 3,
rowLabels=row_label,
colLabels=col_label,
rowColours=row_color,
loc='upper right')
plt.plot(y)
plt.show()
def draw_matrix(image):
import matplotlib.pylab as pl
h, w = image.shape
nx = w
ny = h
data = np.asarray(image, dtype=int)
pl.figure(figsize=(5, 5))
tb = pl.table(cellText=data, loc=(0, 0), cellLoc='center')
tc = tb.properties()['child_artists']
for cell in tc:
cell.set_height(1 / ny)
cell.set_width(1 / nx)
ax = pl.gca()
ax.set_xticks([])
ax.set_yticks([])
plt.show()
def mytable(mypolyfit):
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(18, 4))
data = list(mypolyfit.values.astype(str))
columns = tuple(mypolyfit.columns.astype(str))
rows = np.arange(1, len(mypolyfit) + 1).astype(str)
#values = np.arange(len(mypolyfit))
#value_increment = 1
# Get some pastel shades for the colors
ccolors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))
rcolors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))
#n_rows = len(rows)
# index = np.arange(len(rows)) + 1
#bar_width = 0.4
# Plot bars and create text labels for the table
cell_text = data
# Reverse colors and text labels to display the last value at the top.
rcolors = rcolors[::-1]
ccolors = ccolors[::-1]
# Add a table at the bottom of the axes
the_table = plt.table(
cellText=cell_text,
#colWidths=[1/15] * 15,
rowLabels=rows,
colColours=ccolors,
rowColours=rcolors,
colLabels=columns,
loc='center')
the_table.scale(1, 1)
the_table.auto_set_font_size(False)
the_table.set_fontsize(10)
plt.title('Polynomial degree', fontsize=20)
plt.axis('off')
plt.tight_layout()
plt.show()
def display_numpy_array_as_table(input_array):
# This function displays a 1d or 2d numpy array (matrix).
if input_array.ndim == 1:
num_of_columns, = input_array.shape
temp_matrix = input_array.reshape((1, num_of_columns))
elif input_array.ndim > 2:
print(
"Input matrix dimension is greater than 2. Can not display as table"
)
return
else:
temp_matrix = input_array
number_of_rows, num_of_columns = temp_matrix.shape
fig = plt.figure()
tb = plt.table(cellText=np.round(temp_matrix, 2),
loc=(0, 0),
cellLoc='center')
for cell in tb.properties()['child_artists']:
cell.set_height(1 / number_of_rows)
cell.set_width(1 / num_of_columns)
ax = fig.gca()
ax.set_xticks([])
ax.set_yticks([])
plt.show()
def Sobol():
N = 1000
an_m = 'sobol'
smp_m = 'saltelli'
sa_results = pickle.load(
open("SA_results_" + str(N) + "_" + an_m + "_" + smp_m, "rb"))
names = [
'r1_k1', 'r19_k1', 'r11_k1', 'r6_k1', 'r23_k1', 'r12_k1', 'r24_k1',
'r8_k1', 'r17_k1', 'r10_k1', 'r26_k1', 'r15_k1', 'r21_k1', 'r20_k1',
'r7_k1', 'r29_k1', 'r25_k1', 'r9_k1', 'r18_k1', 'r27_k1', 'r16_k1',
'r22_k1', 'r4_k1', 'r14_k1', 'r13_k1', 'r28_k1', 'r2_k1', 'r5_k1',
'r3_k1', 'default_size', 'c1_size', 'cell_size', '_c1_iron_in_Plasma_0'
]
data_s1 = []
data_st = []
data_s2 = []
banned = ['default_size', 'c1_size', 'cell_size', '_c1_iron_in_Plasma_0']
for out in sa_results.keys():
s1_row = []
st_row = []
s2_row = []
for i, n in enumerate(names):
if n not in banned:
s1_row.append(sa_results[out]['S1'][i])
st_row.append(sa_results[out]['ST'][i])
s2_row.append(sa_results[out]['S2'][i, :29])
data_s1.append(s1_row)
data_st.append(st_row)
data_s2.append(s2_row)
columns = [x.split('_k1')[0] for x in names if x not in banned]
rows = [k.split('_')[2] for k in sa_results.keys()]
n_rows = len(data_s1)
index = np.arange(len(columns))
bar_width = 0.8
for data in [data_s1, data_st]:
colors = pl.cm.tab20(np.linspace(0, 1, n_rows))
y_offset = np.zeros(len(columns))
pl.figure(figsize=(30, 8))
cell_text = []
for row in range(n_rows):
pl.bar(index,
data[row],
bar_width,
bottom=y_offset,
color=colors[row])
y_offset = y_offset + data[row]
cell_text.append(['%.3f' % x for x in data[row]])
# Reverse colors and text labels to display the last value at the top.
colors = colors[::-1]
cell_text.reverse()
the_table = pl.table(cellText=cell_text,
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom')
the_table.auto_set_font_size(False)
the_table.set_fontsize(9)
pl.subplots_adjust(bottom=0.2)
pl.xticks([])
pl.margins(x=0)
if data == data_s1:
pl.savefig('Sobol_S1_results.png', bbox_inches="tight", dpi=300)
elif data == data_st:
pl.savefig('Sobol_ST_results.png', bbox_inches="tight", dpi=300)
pl.clf()
for i in range(n_rows):
row_1 = data_s1[i]
row_T = data_st[i]
for n in range(len(row_1)):
if row_T[n] > row_1[n]: # possible higher order interactions
interactions = list(
np.nonzero(np.nan_to_num(data_s2[i][n]) > 0.05)[0])
if interactions:
print(rows[i], columns[n], row_T[n], row_1[n],
list(np.array(columns)[interactions]),
list(np.array(data_s2[i][n])[interactions]))
def Morris():
N = 1000
an_m = 'morris'
smp_m = 'morris'
sa_results = pickle.load(
open("SA_results_" + str(N) + "_" + an_m + "_" + smp_m, "rb"))
data_mu = []
data_mu_star = []
data_sigma = []
banned = ['default_size', 'c1_size', 'cell_size', '_c1_iron_in_Plasma_0']
for out in sa_results.keys():
mu_row = []
mu_star_row = []
sigma_row = []
for i, n in enumerate(sa_results[out]['names']):
if n not in banned:
mu_row.append(sa_results[out]['mu'][i])
mu_star_row.append(sa_results[out]['mu_star'][i])
sigma_row.append(sa_results[out]['sigma'][i])
data_mu.append(mu_row)
data_mu_star.append(mu_star_row)
data_sigma.append(sigma_row)
columns = [
x.split('_k1')[0] for x in sa_results['iron_in_Plasma_c1']['names']
if x not in banned
]
rows = [k.split('_')[2] for k in sa_results.keys()]
n_rows = len(data_mu)
index = np.arange(len(columns))
bar_width = 0.8
for data in [data_mu_star, data_sigma]:
colors = pl.cm.tab20(np.linspace(0, 1, n_rows))
y_offset = np.zeros(len(columns))
pl.figure(figsize=(30, 8))
cell_text = []
for row in range(n_rows):
pl.bar(index,
data[row],
bar_width,
bottom=y_offset,
color=colors[row])
y_offset = y_offset + data[row]
cell_text.append(['%.3f' % x for x in data[row]])
# Reverse colors and text labels to display the last value at the top.
colors = colors[::-1]
cell_text.reverse()
the_table = pl.table(cellText=cell_text,
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom')
the_table.auto_set_font_size(False)
the_table.set_fontsize(9)
pl.subplots_adjust(bottom=0.2)
pl.xticks([])
pl.margins(x=0)
if data == data_mu_star:
pl.savefig('Morris_mu_star_results.png',
bbox_inches="tight",
dpi=300)
elif data == data_sigma:
pl.savefig('Morris_sigma_results.png',
bbox_inches="tight",
dpi=300)
pl.clf()
#!-*-coding:utf8-*-
import matplotlib.pylab as plt
import numpy as np
plt.figure(0)
ax = plt.gca()
y = np.random.randn(9)
col_labels = ['a', 'b', 'c']
row_labels = ['1', '2', '3']
table_vals = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
row_colors = ['r', 'g', 'b']
plt.table(cellText=table_vals,colWidths=[0.1]*3, \
rowLabels=row_labels,colLabels=col_labels, \
rowColours=row_colors, \
colColours=row_colors,\
loc='upper right')
plt.plot(y)
plt.figure(1)
# 1 1 1
# 2 3 3
# 2 3 3
#将Figure划分为3*3的网格生成子区，起始子区0行0列
axes1 = plt.subplot2grid((3, 3), (0, 0), colspan=3) #跨越3列则第一个子区占据了网格第0行
axes2 = plt.subplot2grid((3, 3), (1, 0), rowspan=2)
axes3 = plt.subplot2grid((3, 3), (1, 1), rowspan=2, colspan=2)
plt.show()
print("cont:", cost_val, "train accuracy:", train_a,
"test accuracy:", a, "step:", step)
accuracy_test_list.append(a)
if train_a > 0.95:
break
saver = tf.train.Saver()
saver.save(sess, './model/ssum_predict')
print(y_test_data)
success = 0
fail = 0
for data in y_test_data:
if data[0] == 0.0:
fail += 1
if data[0] == 1.0:
success += 1
print("success:", success, "fail:", fail)
gat, = plt.plot(step_list, accuracy_test_list, 'ro-')
bat, = plt.plot(step_list, accuracy_train_list, 'bs-')
plt.legend([gat, bat], ['test data', 'train data'], loc=2)
plt.xlabel('number of step')
plt.ylabel('accuracy')
cell_text = []
cell_text.append(["asd"])
plt.table(cellText=cell_text, loc='top')
plt.show()
# -*- coding: utf-8 -*-
import matplotlib.pylab as plt
import numpy as np
plt.figure(1, figsize=(8, 6), dpi=300)
axes = plt.gca()
y = np.random.randn(9)
col_labels = ['col1', 'col2', 'col3']
row_labels = ['row1', 'row2', 'row3']
table_vals = [[11, 12, 13], [21, 22, 23], [28, 29, 30]]
row_colors = ['red', 'gold', 'green']
the_table = plt.table(cellText=table_vals,
colWidths=[0.1] * 3,
rowLabels=row_labels,
colLabels=col_labels,
rowColours=row_colors,
loc='upper right')
plt.text(12, 3.4, 'Table Title', size=8)
plt.plot(y)
plt.show()
ax1.plot(radi, s_fixed_time, label='$t=%0.1f s$' % t)
ax1.legend(title='Theis Solution',
loc='upper right',
fancybox=True,
shadow=True)
ax2.legend(title='Theis Solution',
loc='lower right',
fancybox=True,
shadow=True)
cellText = table_values
ax = plt.subplot(111, frame_on=False)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
the_table = plt.table(cellText=table_values,
colWidths=[.1, .3, .3, .3],
colLabels=col_labels,
cellLoc='center',
colLoc='center',
loc='best')
properties = the_table.properties()
cells = properties['child_artists']
for cell in cells:
cell.set_height(.045)
the_table.auto_set_font_size(False)
the_table.set_fontsize(12)
plt.show()
import matplotlib.pylab as plt
import numpy as np
plt.figure()
axes = plt.gca()
y = np.random.randn(9)
col_labels = ["col1", "col2", "col3"]
row_labels = ["row1", "row2", "row3"]
table_vals = [[11, 12, 13], [21, 22, 23], [28, 29, 30]]
row_colors = ["red", "gold", "green"]
the_table = plt.table(
cellText=table_vals,
colWidths=[0.1] * 3,
rowLabels=row_labels,
colLabels=col_labels,
rowColours=row_colors,
loc="upper right",
)
plt.text(12, 3.4, "Table Title", size=8)
plt.plot(y)
plt.show()
def showMatr(matrix):
table(cellText=matrix,
loc='center',
cellLoc='center',
bbox=[0.2, 0.2, 0.5, 0.5])
show()
